A Practical Introduction to Econometric Methods: Classical and Modern

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Description

Contents

Reviews

Language

English

ISBN

9789766409449

Contents

Foreword

Preface

Introduction

Introduction

Classical and Modern Econometrics

Exercises

Part I: Classical

Chapter 1: The General Linear Regression Model

Models in Economics and Econometrics

Data and Econometric Models

Specifying the Model

Introducing the Error Term

Desirable Properties of the Error Term

The General Linear Regression Model

Ordinary Least Squares

Special Case: k = 2 and x1t = 1 for all t

Numerical Calculation

Forecasting with Econometric Models

The Gauss-Markov Theorem on Least Squares

Proof of Part (1) of the Theorem

Proof of Part (2) of the Theorem

Proof of Part (3) of the Theorem

Understanding the Lessons of the Gauss-Markov Theorem

Exercises

Appendix 1.1: Moments of First and Second Order of Random Variables and Random Vectors

Random Variables

Random Matrices and Vectors

Application to the General Linear Regression Model

Appendix 1.2: Time Series Data for Trinidad and Tobago 1967-1991

Chapter 2: Evaluating the Ordinary Least Squares (OLS) Regression Fit

Some Preliminary Remarks

The Coefficient of Determination and the Adjusted Coefficient of Determination

Confidence Intervals for Coefficients

Significance Tests of Coefficients

Testing the Simultaneous Nullity of the Slope Coefficients

“Economic” Evaluation of Regression Results

Reporting Regression Results

Exercises

Chapter 3: Some Issues in the Application of the General Linear Regression Model

Multicollinearity: the Problem

Multicollinearity: Detection

Multicollinearity: a Solution?

Multicollinearity: an Illustration

Misspecification

Dummy Variables

Illustration Involving a Dummy Variable

Exercises

Chapter 4: Generalized Least Squares, Heteroscedasticity and Autocorrelation

Generalized Least Squares

Properties of the Generalized Least Squares Estimator

Consequences of Using Ordinary Least Squares When u ~ (0, s2V)

GLS Estimation: a Practical Solution?

Ad Hoc Procedures for the Identification of Heteroscedasticity and Autocorrelation

Heteroscedasticity: Some Further Considerations

Heteroscedasticity: Testing for its Presence

The Goldfeld-Quandt Test

The Koenker Test

Illustration of the Koenker Test for Heteroscedasticity

Other Tests for Heteroscedasticity

Estimation in the Presence of Heteroscedasticity

Autocorrelation: The Problem

Autocorrelation: Testing for its Presence Using the Durbin-Watson Statistic

Some Justification for the Mechanism of the Durbin-Watson Test

An Illustration of the Durbin-Watson Test for Autocorrelation

Other Tests for Autocorrelation

Estimation in the Presence of Autocorrelation

The Cochrane-Orcutt Procedure

The Hildreth-Lu Procedure

The EViews Procedure

Autocorrelation and Model Specification: a Word of Caution

Exercises

Chapter 5: Introduction to Dynamic Models

Dynamic Models

Almon’s Polynomial Distributed Lag (PDL) Scheme

Illustration of Almon’s Polynomial Distributed Lag Scheme

The Koyck Transformation

Illustration of the Koyck Transformation

The Partial Adjustment Model

The Adaptive Expectations Model

Error Correction Mechanism (ECM) Models

Illustration of the Error Correction Mechanism Model

Autoregressive Distributed Lag (ADL) Models

Illustration of the Autoregressive Distributed Lag Model

The Durbin Test for Autocorrelation in the Presence of Lagged Endogenous Variables

Illustration of the Durbin h-Test

Exercises

Chapter 6: The Instrumental Variable Estimator

Introduction

Consistent Estimators

Is OLS Consistent?

The Instrumental Variable Estimator

The Errors in Variables Model

Exercises

Chapter 7: The Econometrics of Simultaneous Equation Systems

Introduction

Identification

Identifiability of an Equation and Restrictions on the Structural Form

Conditions of Identifiability of an Equation

Estimation in Simultaneous Equation Models

Consistency of the Two Stage Least Squares Estimator

The Two Stage Least Squares Estimator as an Instrumental Variable Estimator

Equivalence of Two Stage Least Squares and Indirect Least Squares in the Case of an Exactly Identified Equation

Illustration of the Two Stage Least Squares Estimator

Exercises

Chapter 8: Simulation of Econometric Models

Introduction

Dynamic and Static Simulation

Some Useful Summary Statistics

Root Mean Square Error

Mean Absolute (or Mean Difference) Error

The Theil Inequality Coefficient

The Theil Decomposition

Regression and Correlation Measures

Some Illustrations of the Use of Model Simulation

Evaluation of Goodness-of-Fit of Single Equation Systems

Forecasting with Single Equation Systems

Evaluation of Goodness-of-Fit of Multiple Equation Systems

Dynamic Response (Multiplier Analysis) in Multiple Equation Systems

Illustration of Dynamic Response

Forecasting and Policy Simulations with Multiple Equation Systems

Illustration

Exercise

Part II: Modern

Chapter 9: Maximum Likelihood Estimation

Introduction

The Cramer-Rao Lower Bound (CRLB)

Properties of Maximum Likelihood Estimators

Maximum Likelihood Estimation in the General Linear Regression Model

Exercises

Chapter 10: The Wald, Likelihood Ratio and Lagrange Multiplier Tests

Introduction

Defining Restrictions on the Parameter Space

The Likelihood Ratio Test

The Wald Test

The Lagrange Multiplier Test

Illustration: Test of Parameter Redundancy

Illustration: Testing Restrictions on Coefficient Values

Conclusion

Exercises

Chapter 11: Specification (and Other) Tests of Model Authenticity

Introduction

Ramsey’s RESET Test for Misspecification (Due to Unknown Omitted Variables)

Illustration of the Ramsey RESET Test

The Jarque-Bera Test for Normality

Illustration of the Jarque-Bera Test for Normality

The Ljung-Box and Box-Pierce Tests for White Noise

Illustration of the Ljung-Box Test

The White Test for Heteroscedasticity

Illustration of the White Heteroscedasticity Test

The Breusch-Godfrey Test for Serial Correlation

Illustration of the Breusch-Godfrey Test for Serial Correlation

The Chow Test for Structural Breaks

Illustration of the Chow Test for Structural Breaks

Exercises

Chapter 12: Stationarity and Unit Roots

The Concept of Stationarity

Unit Roots: Definition

Looking for Unit Roots: an Informal Approach

Formal Testing for Unit Roots

Exercises

Chapter 13: An Introduction to ARIMA Modelling

Introduction

ARIMA Models

Autoregressive Processes of Order p AR(p)

Moving Average Processes of Order q MA(q)

Autoregressive Moving Average Processes of Order p, q ARMA(p, q)

Autoregressive Integrated Moving Average Processes of Order p, d, q ARIMA(p, d, q)

The Partial Autocorrelation Function (PACF)

Estimating the Autocorrelation and Partial Autocorrelation Functions

Estimation of the Mean

Estimation of the Autocovariance of Order k

Estimation of the Autocorrelation of Order k

Estimation of the Partial Autocorrelation of Order j

Sampling Distributions of and

The Box-Jenkins Iterative Cycle

Identification

Illustrating the Identification of p and q

Estimation and Diagnostic Checking

Illustration of the Estimation and Diagnostic Checking Phases

Forecasting

Illustration of the Forecasting Phase

Seasonal Models

Exercises

Appendix 13.1

Chapter 14: Vector Autoregression (VAR) Modelling with Some Applications

Introduction

Vector AutoregressiON Models

Illustration of vector autoregression estimation using eviews

Evaluation of Vector Autoregression Models

The Impulse Response Function

Variance Decomposition

Forecasting with Vector Autoregression Models

Illustration of Forecasting with Vector Autoregression Models

Vector Autoregression Modelling and Causality Testing

Testing for Causality

Direct Granger Tests

Illustration of Direct Granger Tests

The Sims Test

Exercises

Appendix 14.1

Chapter 15: Cointegration

Introduction

The Vector Error Correction Model (VECM)

The Engle-Granger (EG) Two-Step Procedure

Illustration of the Engle-Granger Two-Step Procedure

Strengths and Weaknesses of the Engle-Granger Two-Step Procedure

The Johansen Procedure

Estimation of a and b

Testing for the Cointegrating Rank r

Illustration of the Johansen Procedure

Cointegration and Causality

Exercises

Appendix 15.1: Critical values for ADF tests of cointegratability

(constant term included in test equations)

Appendices

Statistical Tables

References

Index

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